The advent of e-commerce has profoundly reshaped the global financial landscape, transforming traditional banking operations and customer interactions. In India, a rapidly digitizing economy, the integration of e-commerce principles into the banking sector has ushered in an era of unprecedented convenience, accessibility, and efficiency, epitomized by the rise of e-banking and digital payment solutions. However, this transformative journey is not without its complexities. This article provides a comprehensive analysis of the evolving role of e-commerce within the Indian banking industry, meticulously examining the multifaceted issues and concerns that accompany its widespread adoption. Drawing upon existing literature, the study synthesizes challenges related to cybersecurity, technological infrastructure, customer adoption barriers (including the digital divide), regulatory frameworks, and operational complexities. It highlights the critical need for robust security measures, enhanced digital literacy initiatives, adaptive regulatory policies, and continuous technological innovation to fully harness the potential of e-commerce in fostering a secure, inclusive, and efficient banking ecosystem in India.

This paper reviews artificial intelligence approaches to predicting the risks of employee turnover and managing strategies designed to retain them. The purpose of the current study is to carry out a systematic review and practical assessment of existing algorithms used as early warnings for personnel turnover in corporate environments and to recommend ways through which the derived models could be incorporated into HR management processes. The relevance of this work is determined by organizations’ enormous costs associated with replacing specialists, the rapid growth of the HR analytics market, and the need to shift from a reactive turnover management model to a proactive talent-retention system. The novelty of the research lies in the comprehensive comparison of classical statistical methods (logistic regression, CoxRF) and modern machine learning algorithms (XGBoost, LSTM-RNN, Bidirectional-TCN, graph neural networks) on both proprietary and open datasets, as well as in the incorporation of interpretability criteria (SHAP, LIME), organizational and ethical barriers, MLOps requirements, and EU regulatory standards into the architecture of predictive HR systems. The findings demonstrate that basic statistical models provide a rapid start and clear interpretability on small samples; however, as data volumes grow, gradient boosting emerges as the “gold standard,” and recurrent and convolutional networks become preferable for analyzing temporal communications. Graph neural networks improve flight-risk detection quality by accounting for social connections, while interpretability tools enable the translation of a score into a concrete retention plan. The key takeaway is the need for an integrated approach: starting from detailed data prep and cleanup, building a cross-functional team, setting up an MLOps loop, designing solutions ethically, training end-users, and monitoring success metrics regularly. This paper will be helpful to HR directors, people analytics specialists, AI-in-HR project managers, as well as academic researchers in the field of human capital management.

The article "Impact of Digital Technologies on Brand Product Strategy Development in Ukraine's Oil-and-Fat Market" analyzes the impact of digital technologies on the formation and adaptation of product strategies of Ukrainian brands in the oil-and-fat industry. The study emphasizes the relevance of understanding how the digital environment, growing consumer expectations for transparency and innovation, as well as increased global competition are changing the strategic behavior of companies.

The work uses a mixed methodology that combines bibliometric analysis, market trend study, SWOT analysis, and content analysis of corporate web resources and social networks. This allowed us to explore the role of specific digital tools in shaping modern product strategies. Based on a content analysis of leading Ukrainian brands, the authors proposed a typology of digital strategies used in the sector.

The results indicate that digitalization contributes to the growth of companies' competitiveness through the personalization of offers, transparency in supply chains and optimization of business processes.

Ukrainian brands actively integrate digital technologies into various communication channels and operational activities, demonstrating a high level of readiness for digital transformations. The article contributes to the development of the discourse on the digital modernization of traditional industries and offers practical recommendations for enterprises seeking to update their product strategies through digital integration.

In this article, the problem of the low efficiency of traditional cold communications with venture capital funds is examined. The relevance of the study is determined by the need to develop automated tools for targeted search of relevant investors capable of overcoming the limitations of warm recommendations and expanding access to capital for startup teams without an extensive network. The aim of the paper is to demonstrate an algorithmic approach based on machine learning methods for identifying relevant investors and to investigate the integration of ML ranking with a disciplined multistep-outreach strategy. The novelty lies in the use of a multilayer feature architecture combining an investment graph, thematic embeddings, soft signals from public channels, and dynamic indicators of fund activity, as well as in the construction of a controlled cycle of cold communications with two follow-ups in each three-day window. The obtained results confirm an increase in the efficiency of the cold channel: algorithmic selection enabled maintaining an open rate at the level of 74–80%, a reply rate in the range of 10–17%, and provided 96 scheduled calls per quarter without a single warm recommendation. The integration of the ML ranking model with a structured cadence strategy increases the controllability of the process, turning fundraising from a lottery into a repeatable business process with continuous model learning on feedback data. Practical implementation includes not only the development of an investor ranking model but also the creation of infrastructure for large-scale mailings: configuration of mail domains, optimization of message templates, A/B-testing, and integration with meeting-scheduling tools. This allows startups to systematically increase open, click, and reply rates as well as conversion into negotiations. The article will be useful to startup founders, venture analysts, and fundraising specialists seeking to improve the efficiency of cold communications with investors.

This article examines how enterprise resource planning (ERP) systems are deployed to enhance the efficiency of corporate tax accounting and tax-liability management. Its relevance stems from the growing complexity of tax regulations and the pressing need to automate related processes. We synthesize both empirical and analytical evidence on ERP functionality in the tax domain, covering automated calculation, compliance monitoring, report generation, and tax-planning support. The review includes recent studies reporting shifts in tax-risk levels, reductions in compliance costs, and the evolving role of in-house tax departments. We pay special attention to the implementation risks associated with ERP roll-outs and the ethical boundaries of leveraging advanced analytics. The aim is to identify the enduring impacts of integrating ERP into tax-accounting workflows. Employing comparative and systems-based methodologies alongside case-study and literature analysis, we conclude by underscoring ERP’s significance as a tool for strengthening compliance, transparency, and governance in corporate taxation. Practitioners in corporate finance, systems integrators, and tax-planning specialists will find the insights particularly valuable.